Instructions to use majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K
# Run inference directly in the terminal:
llama cli -hf majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K
# Run inference directly in the terminal:
llama cli -hf majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K

Use Docker

docker model run hf.co/majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K

LM Studio
Jan

vLLM

How to use majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K

Ollama
How to use majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K with Ollama:
```
ollama run hf.co/majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K
```

Unsloth Studio

How to use majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K to start chatting

How to use majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K with Docker Model Runner:
```
docker model run hf.co/majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K
```

Lemonade

How to use majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull majentik/Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K:Q2_K

Run and chat with the model

lemonade run user.Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K-Q2_K

List all available models

lemonade list

majentik commited on 17 days ago

Commit

b1b0de6

verified ·

1 Parent(s): 28f0927

docs: upstream-first KV-cache guidance (q8_0/q4_0, mainline Hadamard rotation); fork demoted to experimental

Browse files

Files changed (1) hide show

README.md +18 -1

README.md CHANGED Viewed

@@ -17,6 +17,23 @@ library_name: gguf
 pipeline_tag: text-generation
 ---
 # Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K
 GGUF Q2_K weight-quantized variant of [nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16) optimised for use with **RotorQuant** KV cache compression via a dedicated llama.cpp fork.
@@ -44,7 +61,7 @@ This model combines two independent compression techniques:
 ## Quickstart
-### Option A — With RotorQuant KV cache (fork required)
 You must build from the RotorQuant-enabled llama.cpp fork:

 pipeline_tag: text-generation
 ---
+> [!TIP]
+> **KV-cache quantization without any fork (recommended, 2026):** upstream
+> llama.cpp/Ollama now cover this natively — use `-ctk q8_0 -ctv q8_0`
+> (~half KV memory, negligible quality loss: perplexity +0.002–0.05) or
+> `-ctk q4_0 -ctv q4_0` (~quarter memory, ≈7.6% perplexity increase). In
+> Ollama: `OLLAMA_KV_CACHE_TYPE=q8_0` with `OLLAMA_FLASH_ATTENTION=1`. Keep
+> K and V types symmetric to stay on the fast fused Flash-Attention path.
+> Since April 2026, mainline llama.cpp also applies Hadamard rotation to
+> KV activations ([PR #21038](https://github.com/ggml-org/llama.cpp/pull/21038)),
+> which greatly improves low-bit KV quality (opt-out:
+> `LLAMA_ATTN_ROT_DISABLE=1`).
+>
+> The RotorQuant/TurboQuant fork flow below is **experimental/legacy**: the
+> TurboQuant llama.cpp PR was closed without merging (June 2026) and the fork
+> is unmaintained relative to mainline. It is NOT required to use this model.
+<!-- kv-upstream-note -->
 # Nemotron-3-Nano-4B-RotorQuant-GGUF-Q2_K
 GGUF Q2_K weight-quantized variant of [nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16) optimised for use with **RotorQuant** KV cache compression via a dedicated llama.cpp fork.
 ## Quickstart
+### Option A — RotorQuant KV cache (experimental fork — not required)
 You must build from the RotorQuant-enabled llama.cpp fork: